The invention discloses a method for resisting an
attack deep neural network based on a
frequency band, and the method comprises the steps: firstly obtaining a
frequency band space which comprises theanti-interference of
frequency band pairs in N directions, wherein the anti-interference of frequency band pairs in each direction comprises the anti-
interference distribution of frequency band pairswith M wavelengths, the anti-
interference distribution of each frequency band pair is a waveform containing alternating activation and inhibition, and gradient signals are filled between wave crestsand wave troughs of each frequency band pair; respectively adding the frequency band pair anti-
interference distribution and the original image to obtain an anti-
attack sample, inputting the anti-
attack sample into the attacked deep neural network, taking the anti-attack sample corresponding to the minimum value of the prediction result
score as an optimal anti-attack sample, and attacking the attacked deep neural network. According to the method, the waveform containing alternate activation and suppression is adopted as anti-interference distribution, so the sensitivity of the network to an anti-attack sample is effectively improved, the anti-attack sample can be easily measured by the deep neural network, the original signals are confused, and the deep neural network is enabled to predict an error result.